Effects of Structure and Composition of Adsorbents on Competitive Adsorption of Gaseous Emissions: Experiment and Modeling

Dangerous gases arising from combustion processes must be removed from the air simply and cheaply, e.g., by adsorption. This work is focused on competitive adsorption experiments and force field-based molecular modeling of the interactions at the molecular level. Emission gas, containing CO, NO, SO2, and CO2, was adsorbed on activated carbon, clay mineral, silicon dioxide, cellulose, or polypropylene at two different temperatures. At 20 °C, activated carbon had the highest NO and SO2 adsorption capacity (120.83 and 3549.61 μg/g, respectively). At 110 °C, the highest NO and SO2 adsorption capacity (6.20 and 1182.46 μg/g, respectively) was observed for clay. CO was adsorbed very weakly, CO2 not at all. SO2 was adsorbed better than NO, which correlated with modeling results showing positive influence of carboxyl and hydroxyl functional groups on the adsorption. In addition to the wide range of adsorbents, the main novelty of this study is the modeling strategy enabling the simulation of surfaces with pores of controllable sizes and shapes, and the agreement of the results achieved by this strategy with the results obtained by more computationally demanding methods. Moreover, the agreement with experimental data shows the modeling strategy to be a valuable tool for further adsorption studies.


Introduction
In recent years, more than ever before, the importance of monitoring the evolution of the amount of pollutants in the environment has increased [1]. People and nature in general need optimal conditions for their efficient functioning, which is related to the reduction of destructive factors and the increase of constructive factors in the environment. Manufacturing industry is accompanied by the disruption of natural resources during the acquisition of input materials, a number of chemical reactions or energy processes during production, and last but not least, the disposal of waste, i.e., unused or unusable products. The main sources of pollution include the manufacturing and processing industry [2] as well as the energy industry [3] and transportation [4], with which the increasing production of specifically air pollutants is connected.
Despite the fact that thermochemical conversion is beginning to be replaced by the use of renewable resources worldwide, the combustion process of various materials (coal, gas, heating oils, wood, biomass, etc.) still contributes significantly to air pollution [5,6]. Even

Characterization of Samples
A photoelectron spectrometer with a hemispherical VG SCIENTA R3000 analyzer (Prevac, Rogów, Poland) was used for XPS analysis of AC. The photoelectron spectra were recorded using a monochromatized aluminum Al Kα source (E = 1486.6 eV) and a low-energy electron flood gun (FS40A-PS). The base pressure in the analysis chamber was 5·10 −9 mbar. Spectra were recorded with a constant pass energy of 100 eV for the survey and high-resolution spectra. The binding energy scale was calibrated using the Au 4f7/2 line of a cleaned gold sample at 84.0 eV. The composition and chemical surrounding of the sample surface were investigated on the basis of the areas and binding energies of O 1s and C 1s photoelectron peaks. The fitting of high-resolution spectra was performed using CasaXPS software.
The moisture content of the samples was determined by Kern ABT 320-4NM (Kern & Sohn GmbH, Balingen, Germany). Particle density was determined by measuring the volume of the sample in water and the weight with MA 110.R laboratory scale (RAD-WAG Váhy s.r.o., Šumperk, Czech Republic). Atmospheric pressure and pressure in the adsorption system were measured with GPB 3300 (GHM-Greisinger s.r.o., Regenstauf, Germany) and Kimo MP 210 (Kimo Instruments, Mumbai, India), respectively. The concentration of NO, SO 2 and CO was measured by the infrared spectrometer PG-350 (Horiba, Palaiseau, France). Nitrogen physisorption at 77 K was measured using 3Flex apparatus (Micromeritics, Norcross, CA, USA). Prior to physisorption analysis, each material was degassed under pressure of 0.6 bar and temperature of 350 • C. This step was done for all materials to release physisorbed moisture. After such pre-treatment, the nitrogen adsorption-desorption isotherms at 77 K of all materials were measured for the relative pressure range p/p 0~1 × 10 −7 -0.99. Nitrogen adsorption-desorption data were processed using BET theory, the t-plot method and the BJH method applied on the adsorption branch of the nitrogen adsorption-desorption isotherm, by using either the Carbon Black STSA standard isotherm (for carbonaceous materials) or Broekhoff-De Boer standard isotherm (for inorganic materials). The slit or cylindrical-pore geometry of mesopores and macropores characterized by pore width or diameter, respectively, were assumed. From measured data, it was possible to reliably obtain the specific surface area, S BET (m 2 /g), the mesopore-macropore surface area, S meso (m 2 /g), the micropore volume, V micro (mmliq 3 /g) and mesopore-macropore size distribution characterized by the pore width or diameter. The net pore volume, V net (mm 3 liq/g), was evaluated from the adsorbed amount of nitrogen at p/p 0~0 .99. The micropore size distribution was evaluated from the adsorbed amount of nitrogen at p/p 0~1 × 10 −7 -0.05 using the Horwath-Kawazoe solution and assuming the slit-pore geometry for both carbonaceous and inorganic materials.
X-ray powder diffraction (XRPD) patterns were recorded under Co Kα irradiation (λ = 0.1789 nm, U = 35 kV, I = 25 mA) using the Bruker D8 Advance diffractometer (Bruker AXS, Karlsruhe, Germany) equipped with a fast position sensitive detector VÅNTEC 1. Measurements were carried out in the reflection mode, powder samples were pressed in a rotational holder, and a goniometer was used with the Bragg-Brentano geometry in 2θ range from 3 to 80 • , step size 0.03 • . The phase composition was evaluated using database PDF 2 Release 2020 (International Centre for Diffraction Data).

Sample Preparation
The powder samples (AC, CL, SiO 2 ) were crushed and sieved into three granulometric fractions-0.16-0.315 mm, 0.315-0.63 mm, 0.63-1.00 mm (Table 1)-to ensure laminar flow in the adsorption chamber (Ad) whose inner tube diameter was 10 mm. CEL and PP were used in their original form, i.e., pellets and nonwoven, respectively. Each type of sample was used in its wet and dried state. The samples were dried in a drying oven Binder FP 400. Particle density and humidity of the adsorbents were determined, and the samples were labeled according to the granulometric fraction (Table 1).

Adsorption System
The adsorption experiments took place in a closed system consisting of PTFE hoses, stainless steel fittings, the stainless steel Ad and analytical instruments ( Figure 1). The Ad was a stainless steel tube with an inner diameter of 10 mm. A volume of 3 cm 3 was calculated from the volume of the catalyst V cat according to the formula: where Q V is volumetric gas flow rate per hour (0.5 L/min = 30,000 cm 3 /h) and GHSV is gas hourly space velocity (10,000 h −1 ) [36]. type of sample was used in its wet and dried state. The samples were dried in a drying oven Binder FP 400. Particle density and humidity of the adsorbents were determined, and the samples were labeled according to the granulometric fraction (Table 1). Table 1. Basic information about the adsorbing materials used, such as particle density and material humidity, and its forms and labeling.

Adsorption System
The adsorption experiments took place in a closed system consisting of PTFE hoses, stainless steel fittings, the stainless steel Ad and analytical instruments ( Figure 1). The Ad was a stainless steel tube with an inner diameter of 10 mm. A volume of 3 cm 3 was calculated from the volume of the catalyst Vcat according to the formula: where QV is volumetric gas flow rate per hour (0.5 L/min = 30,000 cm 3 /h) and GHSV is gas hourly space velocity (10,000 h −1 ) [36]. The gas traveled from the gas cylinders (EG-emission gas, N2-nitrogen) through the pressure gauge (P) and the adsorbent (Ad) to the analyzer (An) at a speed of 0.5 L/min (analyzer parameter). The rest was discharged through the overflow branch (OUT) (Figure 1).
First, the entire adsorption apparatus was filled with nitrogen (including the Ad branch). Subsequently, the source gas was switched from N2 to EG through the branch without Ad. The measurement of the concentration of emission components with the analyzer was switched on. As soon as the concentration values stabilized, thereby determining the actual concentrations of gaseous components in EG, the gas path was switched to the Ad branch, where adsorption took place. The measurement was terminated after the initial concentration was again reached (the actual concentration in the EG bottle). The measurement took place at 20 °C (with wet adsorbents) and 110 °C (with dried adsorbents The gas traveled from the gas cylinders (EG-emission gas, N 2 -nitrogen) through the pressure gauge (P) and the adsorbent (Ad) to the analyzer (An) at a speed of 0.5 L/min (analyzer parameter). The rest was discharged through the overflow branch (OUT) (Figure 1).
First, the entire adsorption apparatus was filled with nitrogen (including the Ad branch). Subsequently, the source gas was switched from N 2 to EG through the branch without Ad. The measurement of the concentration of emission components with the analyzer was switched on. As soon as the concentration values stabilized, thereby determining the actual concentrations of gaseous components in EG, the gas path was switched to the Ad branch, where adsorption took place. The measurement was terminated after the initial concentration was again reached (the actual concentration in the EG bottle). The measurement took place at 20 • C (with wet adsorbents) and 110 • C (with dried adsorbents and Ad in the drying oven Binder FP 400 (Binder GMBh, Tuttlingen, Germany). Each sample was measured three times and the results were averaged. The total amount of each adsorbed gaseous component was obtained by integrating the area between the adsorption curve and the original concentration value of the given emission component, and using the ideal gas equation (where the amount of substance n is expressed as m/M) for expressed mass m of the adsorbed gas component: where p (Pa) is pressure in the adsorption system, V (dm 3 ) is volume of the adsorbent, M (g·mol −1 ) is molar mass of the gaseous component, R (J·K −1 ·mol −1 ) is the gas constant, and T (K) is temperature in the adsorption system [37]. Adsorbed mass m was related to one gram of wet or dried adsorbent expressed in µg/g.

Atomistic Models and Modeling Strategy
Building of the initial models of EG molecule and adsorbent surface, geometry optimizations, molecular dynamics, and energy calculations were carried out in Biovia Materials Studio 7.0 (MS) modeling environment [38]. and Ad in the drying oven Binder FP 400 (Binder GMBh, Tuttlingen, Germany). Each sample was measured three times and the results were averaged. The total amount of each adsorbed gaseous component was obtained by integrating the area between the adsorption curve and the original concentration value of the given emission component, and using the ideal gas equation (where the amount of substance n is expressed as m/M) for expressed mass m of the adsorbed gas component: where p (Pa) is pressure in the adsorption system, V (dm 3 ) is volume of the adsorbent, M (g·mol −1 ) is molar mass of the gaseous component, R (J·K −1 ·mol −1 ) is the gas constant, and T (K) is temperature in the adsorption system [37]. Adsorbed mass m was related to one gram of wet or dried adsorbent expressed in μg/g.
MUS and NON periodic unit cells were enlarged to~50 × 50 Å with a surface containing two clay layers with two interlayers and a vacuum slab added with a height of 350 Å. NO and SO 2 molecules were placed on various spots of the surfaces ( Figure 5). Three models were created for each "EG molecule/adsorbent surface spot" combination. An MS/Forcite module was used for geometry optimization of each model. The COMPASS force field parameterized atoms and assigned their charges [44], which was verified for CEL [45], PP [46], SiO2 [47], and AC [43]. A universal force field, with the QEq module setting charges, was used for models containing MUS and NON [48].
The Smart algorithm (implemented in MS) with 5·10 5 steps was used. Convergence thresholds for displacement, force, and energy were 5·10 −5 Å , 5·10 −3 kcal·mol −1 ·Å −1 , and 1·10 −4 kcal·mol −1 , respectively. Cell parameters were not optimized. Interaction energy (Eint; kcal/mol) was calculated for each optimized model from potential energies (Ep) using the following equation: where Ep1 is Ep of a whole model, Ep2 is Ep of a surface, and Ep3 is Ep of a NO/SO2 molecule. The lower the Eint value was, the stronger the interaction between NO/SO2 and the surfaces. For molecular dynamics, periodic models of AC, MUS, and NON with dimensions of ~17 × 17 Å (α = β = 90°) were prepared. The AC model contained three graphene layers, while the MUS and NON models contained two clay layers with one interlayer. All models had a vacuum slab, 1000 Å high, perpendicular to the surfaces. A total of 28 NO and 28 SO2 molecules were evenly placed on one side of the surfaces. The MD/Forcite module and COMPASS or Universal force field for AC or MUS/NON models, respectively, were used for molecular dynamics with NVT ensemble, Nosé thermostat, random initial velocities, 293 K temperature, and 5 ns total simulation time.

Physisorption
Nitrogen physisorption measurements (Figure 6a,c,e; Table 2) proved that individual groups of adsorbents differ very much concerning their textural properties. According to the surface area and porosity, the adsorbents used can be ordered as follows (from largest to smallest): AC > CL > SiO2 > CEL > PP. An MS/Forcite module was used for geometry optimization of each model. The COMPASS force field parameterized atoms and assigned their charges [44], which was verified for CEL [45], PP [46], SiO 2 [47], and AC [43]. A universal force field, with the QEq module setting charges, was used for models containing MUS and NON [48].
The Smart algorithm (implemented in MS) with 5·10 5 steps was used. Convergence thresholds for displacement, force, and energy were 5·10 −5 Å, 5·10 −3 kcal·mol −1 ·Å −1 , and 1·10 −4 kcal·mol −1 , respectively. Cell parameters were not optimized. Interaction energy (E int ; kcal/mol) was calculated for each optimized model from potential energies (E p ) using the following equation: where E p1 is E p of a whole model, E p2 is E p of a surface, and E p3 is E p of a NO/SO 2 molecule. The lower the E int value was, the stronger the interaction between NO/SO 2 and the surfaces. For molecular dynamics, periodic models of AC, MUS, and NON with dimensions of 17 × 17 Å (α = β = 90 • ) were prepared. The AC model contained three graphene layers, while the MUS and NON models contained two clay layers with one interlayer. All models had a vacuum slab, 1000 Å high, perpendicular to the surfaces. A total of 28 NO and 28 SO 2 molecules were evenly placed on one side of the surfaces. The MD/Forcite module and COMPASS or Universal force field for AC or MUS/NON models, respectively, were used for molecular dynamics with NVT ensemble, Nosé thermostat, random initial velocities, 293 K temperature, and 5 ns total simulation time.

Physisorption
Nitrogen physisorption measurements (Figure 6a,c,e; Table 2) proved that individual groups of adsorbents differ very much concerning their textural properties. According to the surface area and porosity, the adsorbents used can be ordered as follows (from largest to smallest): AC > CL > SiO 2 > CEL > PP. 1.02 * Since adsorbent belongs to meso-macroporous solids, the mesopores surface area Smeso equals the specific surface area SBET. AC1-3, CL1-3, SiO21-3-activated carbon, clay, and silicon dioxide, respectively (1-3-three granulometric fractions; see Table 1), CEL-cellulose, PP-polypropylene; SBETspecific surface area, Smeso-mesopore-macropore surface area, Vmicro-micropore volume, Vnet-net pore volume, n. d.-not defined.   Table 1), V a -adsorbed volume of nitrogen, STP-standard temperature and pressure, p/p 0 -relative pressure, dV/dlog(w)-derivation of adsorbed volume of nitrogen divided by derivation of logarithm of pore width, dV/dw pore -derivation of adsorbed volume of nitrogen divided by derivation of pore width, dV/dlog(d)-derivation of adsorbed volume of nitrogen divided by derivation of logarithm of pore diameter.
AC showed the type I adsorption isotherm according to IUPAC classification [49], typical for microporous materials (Figure 6a). Evaluated textural parameters (Table 2) and pore-size distributions (Figure 6b and insets) definitively correspond to this feature. AC showed micropores of 0.48 nm width with a volume of 359-376 mm 3 liq/g, and mesopore surface area in the range of 114-209 m 2 /g. With increasing particle-size fraction in the range of 0.16-1 mm, the mesopore surface area and net pore volume of AC logically decreases with no effect on micropore volume.
Concerning SiO 2 , these samples show the type III adsorption isotherm (according to IUPAC classification) with narrow steep hysteresis loop (Figure 6e), typical for macroporous/nonporous materials with large macropores, which corresponds to the pore-size distributions shown in Figure 6f, proving the presence of macropores having diameter >100 nm. Textural properties (i.e., pore surface area, net pore volume) of SiO 2 are comparable; there are no differences among particle-size fractions. PP and CEL belong among nonporous materials with very low surface area.

Emission Gas Adsorption
With the exception of PP, all adsorbent materials were able to adsorb at least one of the EG components (Table 3). CO 2 was adsorbed on none of the adsorbents. Adsorption of CO on SiO 2 was not observed at any temperature. Granulometry had a significant effect on the adsorbed amount of gases-the smaller the particles, the more adsorbed EG. At the adsorption temperature of 20 • C, AC on average adsorbed the most amount of both NO (9× higher than CL, 20× higher than CEL; Table 3) and SO 2 (1.5× higher than CL, 3× higher than CEL, and 54× higher than SiO 2 ; Table 3). At the adsorption temperature of 110 • C, all of the adsorbents adsorbed NO in very small amounts in contrast to SO 2 , which was adsorbed significantly more on average, and most on CL (6× higher than on AC, 7× higher than on CEL and 82× higher than on SiO 2 ; Table 3). CEL was the only one to adsorb CO at 20 • C (33.55 µg/g). on average, CL adsorbed the most amount of CO at 110 • C.
During the adsorption of NO on AC, there was a significant competitive effect between NO and SO 2 at both temperatures (Figures 7 and 8). As soon as most of the AC adsorption sites were occupied by SO 2 molecules and the adsorbed amount began to decrease (t = 550-950 s at 20 • C, 40-70 s at 110 • C; Figure 7), SO 2 began to replace NO molecules, resulting in NO forced desorption. The more porous the AC (smaller granulometric fraction), the earlier the desorption effect occurs (represented by AC1, AC2, and AC3 borderlines in Figure 7). During the adsorption of NO on AC, there was a significant competitive effect between NO and SO2 at both temperatures (Figures 7 and 8). As soon as most of the AC adsorption sites were occupied by SO2 molecules and the adsorbed amount began to decrease (t = 550-950 s at 20 °C, 40-70 s at 110 °C; Figure 7), SO2 began to replace NO molecules, resulting in NO forced desorption. The more porous the AC (smaller granulometric fraction), the earlier the desorption effect occurs (represented by AC1, AC2, and AC3 borderlines in Figure 7).  Table 1).
At the temperature of 20 °C, a total of 214.32/92.66 μg/g of NO was adsorbed/desorbed on AC1, 163.92/60.86 μg/g on AC2, and 106.51/30.03 μg/g on AC3 (Figure 8). This phenomenon was investigated in more detail using molecular dynamics (see Section 3.3). Forced desorption was not observed with any other adsorbent.  Table 1).
At the temperature of 20 • C, a total of 214.32/92.66 µg/g of NO was adsorbed/desorbed on AC1, 163.92/60.86 µg/g on AC2, and 106.51/30.03 µg/g on AC3 (Figure 8). This phenomenon was investigated in more detail using molecular dynamics (see Section 3.3). Forced desorption was not observed with any other adsorbent.
In the case of AC, the adsorption maximum of NO was reached gradually (Figure 8), while the SO 2 adsorption maximum of SO 2 occurred immediately after the EG flow started (Figure 9). The same was observed during SO 2 adsorption on CL, SiO 2 and CEL (Figures 10 and 11). Nanomaterials 2023, 13, x FOR PEER REVIEW 13  Table 1).  Table 1).
Nanomaterials 2023, 13, x FOR PEER REVIEW 14 In the case of AC, the adsorption maximum of NO was reached gradually (Figu while the SO2 adsorption maximum of SO2 occurred immediately after the EG flow st (Figure 9). The same was observed during SO2 adsorption on CL, SiO2 and CEL (Fi 10 and 11).  Table 1) Figure 9. The adsorption curves showing SO 2 adsorption on AC at adsorbent temperatures of (a) 20 • C and (b) 110 • C. AC1-3-activated carbon at different granulometric fractions (see Table 1).  Table 1). Figure 10. The adsorption curves showing SO 2 adsorption on CL at adsorbent temperatures of (a) 20 • C and (b) 110 • C. CL1-3-clay at different granulometric fractions (see Table 1).

Molecular Modeling
CO2 adsorption was not observed on any of the adsorbents, and CO was onl sorbed in negligible amounts and only at a temperature of 110 °C. Therefore, these EG components were not molecularly modeled. Molecular dynamics clarified the petitive adsorption process of NO and SO2 on the most adsorbing materials, i.e., AC ing the most specific adsorption process; Figure 12) and CL (MUS and NON). It sho  Table 1), CEL-cellulose.

Molecular Modeling
CO 2 adsorption was not observed on any of the adsorbents, and CO was only adsorbed in negligible amounts and only at a temperature of 110 • C. Therefore, these two EG components were not molecularly modeled. Molecular dynamics clarified the competitive adsorption process of NO and SO 2 on the most adsorbing materials, i.e., AC (having the most specific adsorption process; Figure 12) and CL (MUS and NON). It showed NO forced desorption caused by SO 2 adsorption. Consequently, it was found with geometry optimization that SO 2 showed a lower E int with the surfaces than NO (Figure 13a,b), which correlated with the molecular dynamics results. The SO 2 molecules gradually occupied the top of the first surface, and the bottom of the second one. According to the notation "EG molecule_EG molecules number on the top of the surface/on the bottom of the surface_in the vacuum slab", there were SO 2 _12/13_3 on AC, SO 2 _10/9_9 on MUS, and SO 2 _7/7_14 on NON (Figure 12), while most NO molecules were expelled away from the surfaces (NO_3/3_22 for AC, NO_0/2_26 for MUS, and NO_1/1_26 for NON; Figure 12). The molecular dynamics results correspond to the experimentally determined sorption capacities, when higher sorption capacities were measured for SO 2 than for NO; and at 20 • C (293 K), AC showed higher sorption capacity than CL (MUS/NON; Table 3). Due to the established correlation of molecular dynamics and geometry optimization results, the other models were only geometry optimized with one EG molecule on the surfaces in order to determine Eint and observe molecular interactions in more detail.
Geometry optimization showed the suitability of the chemical composition of all sorbent materials for NO and SO2 adsorption. An attractive interaction with NO or SO2 was observed for all models containing AC. Average Eint of models containing NO was 2× in the interlayer of clay minerals causing a strong interaction between clay layers, and making it impossible for NO or SO2 to penetrate. At the same time, these cations occupied all chemically suitable sites for interaction with NO and SO2. The average Eint of models containing SO2 on MUS and NON is significantly lower than in the case of models with NO.  Due to the established correlation of molecular dynamics and geometry optimization results, the other models were only geometry optimized with one EG molecule on the surfaces in order to determine E int and observe molecular interactions in more detail.
Geometry optimization showed the suitability of the chemical composition of all sorbent materials for NO and SO 2 adsorption. An attractive interaction with NO or SO 2 was observed for all models containing AC. Average E int of models containing NO was 2× higher than models containing SO 2 on AC (Figure 13a). NO interacted most strongly with the cavities, cavity_COOH (E int = −8.312 kcal/mol) and cavity_OH. The second strongest NO interactions were observed with the AC corner surfaces (E int = −5.962 kcal/mol). In the case of SO 2 , the strongest interactions were observed for cavity_COOH (E int = −15.360 kcal/mol) and cavity_OH. Corner_COOH (E int = −13.877 kcal/mol) showed the second strongest interactions with SO 2 . These findings prove that the interaction of NO/SO 2 with AC is stronger, the more numerous surface atoms participating in the non-bonding interaction. The presence of the functional groups (mainly -COOH) is another important factor supporting the strength of AC interaction with NO/SO 2 via hydrogen bonds.
NO showed the strongest interaction with the MUS surface (E int = −28.785 kcal/mol) and NON interlayer (E int = −16.177 kcal/mol). The lowest E int showed the model containing SO 2 at the MUS edge (E int = −52.357 kcal/mol); and the second strongest SO 2 interaction was with MUS and NON edge (Figure 13b). The MUS and NON interlayers in most cases showed a positive E int (repulsive force) with NO and SO 2 , due to a large number of cations in the interlayer of clay minerals causing a strong interaction between clay layers, and making it impossible for NO or SO 2 to penetrate. At the same time, these cations occupied all chemically suitable sites for interaction with NO and SO 2 . The average E int of models containing SO 2 on MUS and NON is significantly lower than in the case of models with NO.
All models containing NO placed at all sites of the CEL, PP and SiO 2 surfaces showed a weak negative interaction energy. The strongest interaction (E int = −10.936 kcal/mol; Figure 14a) was achieved in the case of NO lying in the corner of SiO 2 . The second lowest E int (−8.137 kcal/mol; Figure 14a) was achieved for NO placed in the CEL cavity. SO 2 interacted with CEL, PP and SiO 2 surfaces several times stronger. The model with the strongest interaction of SO 2 in the CEL cavity showed 5× lower E int (−55.491 kcal/mol; Figure 14b) than the model with the strongest interaction with NO. The second strongest interaction (E int = −27.872 kcal/mol; Figure 14b) was shown by SO 2 on the SiO 2 bottom. The average E int of models containing NO/SO 2 on PP was the highest.

Adsorption Factors
The importance of chemical (Eint) and textural (SBET, Smeso, Vmicro, Vnet) properties on the adsorption capacity of adsorbents was determined (Table 4). AC adsorption capacity was high for both NO and SO2, which was mainly correlated with the large surface area in spite of high Eint values (i.e., weak interactions). The high CL adsorption capacity was due to a combination of both chemical and textural properties. A dominant factor of low SiO2 and PP adsorption capacity was primarily textural properties (despite strong interactions). The high CEL adsorption capacity was mainly provided by the presence of functional groups strongly interacting with SO2 molecules. Furthermore, agreement between the experiment and molecular modeling was generally more common for lower sorption capacities for NO (corresponding to higher Eint values, i.e., weaker interaction) compared to higher SO2 capacities (corresponding to lower Eint values, i.e., stronger interaction).
In general, a significantly lower Eint can be observed for models containing a molecule in a molecular pore (mainly CEL), which appears to be an ideal combination of chemical (more non-bonding interactions) and structural/steric (molecule size correlates with pore size) factors. The agreement of the force field-based molecular modeling results and experimental data demonstrate the modeling strategy used to be a suitable tool for analysis and prediction of interactions during the sorption process at the molecular level.
However, other modeling studies performed using more computationally demanding DFT methods have led to similar results for interactions between AC and NO/SO2

Adsorption Factors
The importance of chemical (E int ) and textural (S BET , S meso , V micro , V net ) properties on the adsorption capacity of adsorbents was determined (Table 4). AC adsorption capacity was high for both NO and SO 2 , which was mainly correlated with the large surface area in spite of high E int values (i.e., weak interactions). The high CL adsorption capacity was due to a combination of both chemical and textural properties. A dominant factor of low SiO 2 and PP adsorption capacity was primarily textural properties (despite strong interactions). The high CEL adsorption capacity was mainly provided by the presence of functional groups strongly interacting with SO 2 molecules. * denotes dominant adsorption factor, i.e., chemical (E int ) or textural (S BET , S meso , V micro , V net ). AC-activated carbon, CL-clay, SiO 2 -silicon dioxide, CEL-cellulose, PP-polypropylene, S BET -specific surface area, S mesomesopore-macropore surface area, V micro -micropore volume, V net -net pore volume, n. d.-not defined, E intinteraction energy.
Furthermore, agreement between the experiment and molecular modeling was generally more common for lower sorption capacities for NO (corresponding to higher E int values, i.e., weaker interaction) compared to higher SO 2 capacities (corresponding to lower E int values, i.e., stronger interaction).
In general, a significantly lower E int can be observed for models containing a molecule in a molecular pore (mainly CEL), which appears to be an ideal combination of chemical (more non-bonding interactions) and structural/steric (molecule size correlates with pore size) factors. The agreement of the force field-based molecular modeling results and experimental data demonstrate the modeling strategy used to be a suitable tool for analysis and prediction of interactions during the sorption process at the molecular level.
However, other modeling studies performed using more computationally demanding DFT methods have led to similar results for interactions between AC and NO/SO 2 [32,34]. Wang et al. [34] reported the same behavior of NO on AC-the strongest interactions were observed on AC basal plane (compare with "side" in Figure 13a), weaker interactions with -OH group (compare with "bottom_OH" in Figure 13a), and the weakest with -H (compare with "bottom" in Figure 13a). Zhao et al. [32] found the same E int trend for SO 2 on AC, where the molecule interacted most strongly with -COOH groups (compare with "bottom_COOH" in Figure 13a), more weakly with -OH (compare with "bottom_OH" in Figure 13a), and the weakest with -H (compare with "bottom" in Figure 13a).
The comparison demonstrates the usefulness of computationally less demanding force field-based molecular modeling in adsorption research. It also reveals that there is no need to take into account the partial amorphousness of AC, e.g., by a large amount of polyaromatic hydrocarbons [32]. Approximation of AC using graphite, which additionally enables the preparation of pores of the required size, is sufficient for the given purpose.

Conclusions
In this work, the competitive adsorption of emission gas, containing CO, NO, SO 2 and CO 2 , on adsorption materials with different chemical compositions was studied.
CO 2 was not adsorbed on any material. CO was adsorbed in small amounts and mainly at a temperature of 110 • C on AC, CL and CEL. At 20 • C, the AC showed the highest NO and SO 2 sorption capacity. At 110 • C, the highest NO and SO 2 sorption capacity was found for the CL.
The strongest interactions were exhibited between NO/SO 2 and corner or cavity surface types due to the larger number of interacting atoms. Carboxyl and hydroxyl functional groups strengthened the interaction of AC mainly with SO 2 , where hydrogen bonds were formed. NO and SO 2 showed attractive interactions with MUS and NON mainly on the surface and edge. Strong interactions between clay layers and interlayer cations prevented the penetration of NO and SO 2 into the interlayer space.
The dominant factor determining the sorption capacity of AC, SiO 2 and PP was textural (surface area, porosity)-while in the case of CEL, it was the chemical factors (chemical composition, functional groups). Both textural and chemical factors played an important role in adsorption on CL.
The effects of competitive adsorption of emission components on sorption capacity were determined and further clarified using force field-based molecular modeling. The modeling strategy made it possible to simulate surfaces with different and controllable structural character. Geometry optimization and molecular dynamics results agreed well with experimental data allowing the modeling strategy to be further used in studies of interactions during adsorption, or for a prediction of adsorption phenomena.